Robust Nonlinear Control for the Fully Actuated Hexa-Rotor: Theory and Experiments

Flores, Gerardo; de, Andres Montes; Flores, Alejandro

doi:10.1109/lcsys.2022.3188517

Cited by 20 publications

(5 citation statements)

References 27 publications

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“…Finally, comparative simulation experiments and data comparison are set up to prove the effectiveness and superiority of the controller designed in this study. In addition, referring to the current mature physical experiment platform simulation method [54], establishing a ground suspension experiment platform for the motion control of spacecraft tumbling target is also one of our research goals, which further demonstrates and expands the research results in this study.…”

Section: Discussionmentioning

confidence: 58%

Intelligent Attitude Fault-Tolerant Control of Space Tumbling Target Fly-Around Based on RBF Neural Network

Liang

Zhang

2023

IEEE Access

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The flying around monitoring task of space tumbling target is one of the key links of its on-orbit service. Considering the practical constraints of system inertia uncertainty, external disturbance, actuator saturation, and fault in engineering practice, a robust composite controller based on radial basis function (RBF) neural network is proposed. First, in a new line of sight rotation (RLOS) coordinate system, the relative attitude kinematics and dynamics equations between the tracker and tumbling target based on the error quaternion are established; second, the RBF neural network is used to estimate the additive and multiplicative faults of the system, and the fast nonsingular terminal sliding mode surface (FNTSMS) is combined with the active disturbance rejection control (ADRC) technology to design a finite-time faulttolerant control (FTC) strategy with high accuracy, strong robustness, and anti-saturation based on the RBF neural network. It is proven that the designed robust fault-tolerant controller can ensure that the system state error converges to a small region containing the origin in a limited time under the Lyapunov framework. Finally, the effectiveness and superiority of the control strategy were verified by numerical simulation.

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Section: Discussionmentioning

confidence: 58%

Intelligent Attitude Fault-Tolerant Control of Space Tumbling Target Fly-Around Based on RBF Neural Network

Liang

Zhang

2023

IEEE Access

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“…Hence, the approaches that employ approximated system dynamics use external and internal disturbances alongside approximated dynamics 19 , 26 – 28 . Also, recent advancements in adaptive robust nonlinear control-based techniques 29 , 30 have demonstrated promising results for controlling aerial vehicles. Most of these techniques can be categorized under different variations of receding horizon-based control.…”

Section: Related Workmentioning

confidence: 99%

Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles

Kulathunga,

Hamed,

Klimchik

2024

Sci Rep

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This paper presents a technique to model the residual dynamics between a high-level planner and a low-level controller by considering reference trajectory tracking in a cluttered environment as an example scenario. We focus on minimising residual dynamics that arise due to only the kinematical modelling of high-level planning. The kinematical modelling is sufficient for such scenarios due to safety constraints and aggressive manoeuvres that are difficult to perform when the environment is cluttered and dynamic. We used a simplified motion model to represent the motion of the quadrotor when formulating the high-level planner. The Sparse Gaussian Process Regression-based technique is proposed to model the residual dynamics. Recently proposed Data-Driven MPC is targeting aggressive manoeuvres without considering obstacle constraints. The proposed technique is compared with Data-Driven MPC to estimate the residual dynamics error without considering obstacle constraints. The comparison results yield that the proposed technique helps to reduce the nominal model error by a factor of 2 on average. Further, the proposed complete framework was compared with four other trajectory-tracking approaches in terms of tracking the reference trajectory without colliding with obstacles. The proposed approach outperformed the others with less flight time without losing computational efficiency.

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“…Specifically, a disturbance observer-based sliding mode controller was proposed in [44]. In [45], a robust controlbased backstepping technique for the position subsystem and a geometric control for the attitude subsystem were presented to improve system robustness. In [46], the authors developed a feedback PID strategy to control the designed fully actuated system.…”

Section: Example 4 (Quadrotor Attitude Tracking System)mentioning

confidence: 99%

Observer-Based Adaptive Finite-Time Neural Control for Constrained Nonlinear Systems With Actuator Saturation Compensation

Liu,

Yang,

Jiao

et al. 2024

IEEE Trans. Instrum. Meas.

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Robust Nonlinear Control for the Fully Actuated Hexa-Rotor: Theory and Experiments

Cited by 20 publications

References 27 publications

Intelligent Attitude Fault-Tolerant Control of Space Tumbling Target Fly-Around Based on RBF Neural Network

Intelligent Attitude Fault-Tolerant Control of Space Tumbling Target Fly-Around Based on RBF Neural Network

Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles

Observer-Based Adaptive Finite-Time Neural Control for Constrained Nonlinear Systems With Actuator Saturation Compensation

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